A cluster analysis of basketball players for each of the five traditionally defined positions

Determining the players’ playing styles and bringing the right players together are very important for winning in basketball. This study aimed to group basketball players into similar clusters according to their playing styles for each of the traditionally defined five positions (point guard (PG), shooting guard (SG), small forward (SF), power forward (PF), and center (C)). This way, teams would be able to identify their type of players to help them determine what type of players they should recruit to build a better team. The 17 game-related statistics from 15 seasons of the National Basketball Association (NBA) were analyzed using a hierarchical clustering method. The cluster validity indices (CVIs) were used to determine the optimum number of groups. Based on this analysis, four clusters were identified for PG, SG, and SF positions, while five clusters for PF position and six clusters for C position were established. In addition to the definition of the created clusters, their individual achievements were examined based on three performance indicators: adjusted plus-minus (APM), average points differential, and the percentage of clusters on winning teams. This study contributes to the evaluation of team compatibility, which is a significant part of winning, as it allows one to determine the playing styles for each position, while examining the success of position pair combinations.

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Two cluster validity indices for the LAMDA clustering method

Applied Soft Computing ◽

10.1016/j.asoc.2020.106102 ◽

2020 ◽

Vol 89 ◽

pp. 106102 ◽

Cited By ~ 1

Author(s):

Javier Fernando Botía Valderrama ◽

Diego José Luis Botía Valderrama

Keyword(s):

Cluster Validity ◽

Clustering Method ◽

Cluster Validity Indices ◽

Validity Indices

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An Unsupervised Hybrid Symbolic Fuzzy Clustering Approach for Efficient Sclera Segmentation

International Journal of Natural Computing Research ◽

10.4018/ijncr.2021010101 ◽

2021 ◽

Vol 10 (1) ◽

pp. 1-14

Author(s):

B. S. Harish ◽

M. S. Maheshan ◽

C. K. Roopa ◽

S. V. Aruna Kumar

Keyword(s):

Fuzzy Clustering ◽

Clustering Methods ◽

Cluster Validity ◽

Clustering Method ◽

Cluster Validity Indices ◽

Validity Indices ◽

Fuzzy Clustering Methods ◽

Clustering Approach ◽

Representation Method ◽

Segmentation Task

This article performs the sclera segmentation task by proposing a new hybrid symbolic fuzzy c-means (HSFCM) clustering method. Practically, though the data point exhibits some sort of similarity, unfortunately they are not undistinguishable and exhibit some sort of dissimilarity. Thus, to capture these disparities, the proposed work uses symbolic interval valued representation method. Further, to handle uncertainty and imprecision, the paper has proposed to use symbolic fuzzy clustering methods. To assess the performance of the proposed method, extensive experimentation is conducted on SSRBC2016 dataset. The proposed clustering method is compared with existing FCM, KFCM, RSKFCM method in terms of cluster validity indices and accuracy. The obtained outcomes demonstrated that the proposed method performed better compared to the contemporary methods.

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AutoClust: A Framework for Automated Clustering Based on Cluster Validity Indices

2020 IEEE International Conference on Data Mining (ICDM) ◽

10.1109/icdm50108.2020.00153 ◽

2020 ◽

Author(s):

Yannis Poulakis ◽

Christos Doulkeridis ◽

Dimosthenis Kyriazis

Keyword(s):

Cluster Validity ◽

Cluster Validity Indices ◽

Validity Indices

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On fuzzy cluster validity indices for the objects of mixed features

2009 IEEE International Conference on Fuzzy Systems ◽

10.1109/fuzzy.2009.5277190 ◽

2009 ◽

Cited By ~ 2

Author(s):

Mahnhoon Lee

Keyword(s):

Fuzzy Cluster ◽

Cluster Validity ◽

Cluster Validity Indices ◽

Validity Indices ◽

Mixed Features

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Number of Clusters and the Quality of Hybrid Predictive Models in Analytical CRM

Studies in Logic, Grammar and Rhetoric ◽

10.2478/slgr-2014-0022 ◽

2014 ◽

Vol 37 (1) ◽

pp. 141-157 ◽

Cited By ~ 1

Author(s):

Mariusz Łapczyński ◽

Bartłomiej Jefmański

Keyword(s):

Predictive Models ◽

Cluster Validity ◽

Number Of Clusters ◽

Model Combining ◽

Cluster Validity Indices ◽

Validity Indices ◽

And Cluster Analysis ◽

Analytical Tools ◽

F Measure

Abstract Making more accurate marketing decisions by managers requires building effective predictive models. Typically, these models specify the probability of customer belonging to a particular category, group or segment. The analytical CRM categories refer to customers interested in starting cooperation with the company (acquisition models), customers who purchase additional products (cross- and up-sell models) or customers intending to resign from the cooperation (churn models). During building predictive models researchers use analytical tools from various disciplines with an emphasis on their best performance. This article attempts to build a hybrid predictive model combining decision trees (C&RT algorithm) and cluster analysis (k-means). During experiments five different cluster validity indices and eight datasets were used. The performance of models was evaluated by using popular measures such as: accuracy, precision, recall, G-mean, F-measure and lift in the first and in the second decile. The authors tried to find a connection between the number of clusters and models' quality.

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Assessment of Twitter Data Clusters with Cosine-Based Validation Metrics Using Hybrid Topic Models

Ingénierie des systèmes d information ◽

10.18280/isi.250606 ◽

2020 ◽

Vol 25 (6) ◽

pp. 755-769

Author(s):

Noorullah R. Mohammed ◽

Moulana Mohammed

Keyword(s):

Data Clustering ◽

Topic Models ◽

Cluster Validity ◽

Text Documents ◽

Text Data ◽

Validity Assessment ◽

Text Document ◽

Cluster Validity Indices ◽

Validity Indices ◽

Data Clusters

Text data clustering is performed for organizing the set of text documents into the desired number of coherent and meaningful sub-clusters. Modeling the text documents in terms of topics derivations is a vital task in text data clustering. Each tweet is considered as a text document, and various topic models perform modeling of tweets. In existing topic models, the clustering tendency of tweets is assessed initially based on Euclidean dissimilarity features. Cosine metric is more suitable for more informative assessment, especially of text clustering. Thus, this paper develops a novel cosine based external and interval validity assessment of cluster tendency for improving the computational efficiency of tweets data clustering. In the experimental, tweets data clustering results are evaluated using cluster validity indices measures. Experimentally proved that cosine based internal and external validity metrics outperforms the other using benchmarked and Twitter-based datasets.

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